{
 "metadata": {
  "name": "CommitDataForChapter1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To replace the data in chapter 1 with this real time data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from requests import get\n",
      "response = get('https://api.github.com/repos/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/stats/commit_activity').json()\n",
      "weekly_totals = np.array(map(lambda x: x['total'], response))\n",
      "weekly_totals = weekly_totals[np.where(weekly_totals)[0]]  # gives me 52 weeks, but project started < 1 year ago so it backwards fills with 0s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_data = weekly_totals\n",
      "n_count_data = len(weekly_totals)\n",
      "\n",
      "plt.bar(range(n_count_data), weekly_totals);\n",
      "print weekly_totals\n",
      "print n_count_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[13 16  7 26  5 46 25  6  4 28 19 22 13 28  7 10  8 15 13  6 36  5  4  8  8\n",
        "  3  2 11 20  9  1]\n",
        "31\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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wk8xil5dFHjk8q7K4G1nk0TelGwAAGlO6B8JOMotdXhZ55PCsyuJuZJFH35RuAABoTOke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\nYII=\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pymc as pm\n",
      "\n",
      "\n",
      "alpha = 1.0 / count_data.mean()  # Recall count_data is the\n",
      "                               # variable that holds our txt counts\n",
      "\n",
      "lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n",
      "lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n",
      "\n",
      "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@pm.deterministic\n",
      "def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n",
      "    out = np.zeros(n_count_data)\n",
      "    out[:tau] = lambda_1  # lambda before tau is lambda1\n",
      "    out[tau:] = lambda_2  # lambda after tau is lambda2\n",
      "    return out"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "observation = pm.Poisson(\"obs\", lambda_, value=count_data, observed=True)\n",
      "\n",
      "model = pm.Model([observation, lambda_1, lambda_2, tau])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Mysterious code to be explained in Chapter 3.\n",
      "mcmc = pm.MCMC(model)\n",
      "mcmc.sample(40000, 10000, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "[****************100%******************]  40000 of 40000 complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lambda_1_samples = mcmc.trace('lambda_1')[:]\n",
      "lambda_2_samples = mcmc.trace('lambda_2')[:]\n",
      "tau_samples = mcmc.trace('tau')[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(12.5, 10)\n",
      "# histogram of the samples:\n",
      "\n",
      "ax = plt.subplot(311)\n",
      "ax.set_autoscaley_on(False)\n",
      "\n",
      "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
      "         label=\"posterior of $\\lambda_1$\", color=\"#A60628\", normed=True)\n",
      "plt.legend(loc=\"upper left\")\n",
      "plt.title(r\"Posterior distributions of the variables \\\n",
      "    $\\lambda_1,\\;\\lambda_2,\\;\\tau$\")\n",
      "\n",
      "plt.xlabel(\"$\\lambda_1$ value\")\n",
      "\n",
      "ax = plt.subplot(312)\n",
      "ax.set_autoscaley_on(False)\n",
      "\n",
      "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
      "         label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", normed=True)\n",
      "plt.legend(loc=\"upper left\")\n",
      "\n",
      "plt.xlabel(\"$\\lambda_2$ value\")\n",
      "\n",
      "plt.subplot(313)\n",
      "\n",
      "\n",
      "#w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n",
      "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n",
      "         label=r\"posterior of $\\tau$\",\n",
      "         color=\"#467821\", rwidth=2.)\n",
      "plt.xticks(np.arange(n_count_data))\n",
      "\n",
      "plt.legend(loc=\"upper left\")\n",
      "plt.ylim([0, .75])\n",
      "plt.xlabel(\"$\\tau$ (in days)\")\n",
      "plt.ylabel(\"probability\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.text.Text at 0x1155ed950>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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CqLs09XRDpw/rbvz0MT4Zhpl93qyB192Vi/nKw2zlYbbyMFt5mK1psehG3r6N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\nUZ2Yqv8hAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_count_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "31"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lambda_2_samples"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "array([ 7.7774429 ,  7.7774429 ,  7.7774429 , ...,  8.21626815,\n",
        "        8.21626815,  8.80155957])"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}